Abstract
Collaborative filtering algorithm is a widely used recommendation algorithm. However, when applied to e-commerce personalized recommendation, it faces the following issues: firstly, how to consider the user's interest changes over time when getting similarity between the users more precise; secondly, how to use social networks to more accurately getting the nearest neighbor of users; and thirdly, how to consider the behavior of users who have the same interests and different ratings in making the predicted rating score of item more accurately; fourthly, how to use the inherent relation between product categories, such as internal relations, while recommending. In order to solve these problems, this paper improves the traditional collaborative filtering algorithm by integrating timing updates, trust relationship, optimization of predicted rating score and structured ideas. To distinguish users' past interest characteristics and their recent ones, by introducing the idea of timing update, this paper regards the user's shopping experience as a set of time periods, considering the influences of the users' interest at different time on the similarity of the users, and the influence of trust relationship between target user and similar users on the establishment of nearest neighbor set. On this basis, faced with the difference of evaluation criteria of different users on the same recommendation item, this study optimizes scoring method of similar users and gets a pre-scoring-based predicted rating score method for target user to recommend item. Furtherly, considering the relationship between the recommended item and other items, this paper also proposes an idea of relative recommending based on recommended item as a secondary recommendation. At the end of this paper, the proposed method is verified on the review dataset in MovieLens which is provided by the College of computer science and engineering of University of Minnesota. The experimental results show that the proposed method has obvious recommendation accuracy compared with the traditional collaborative filtering algorithm.
Highlights
With the competition between the e-commerce enterprises becomes intensified, personalized recommendation technology, which brings great benefits to e-commerce enterprises, has caught more and more attention
The results show that the recommendation accuracy of the proposed method is higher than that of the traditional collaborative filtering algorithm, and as the time series period increases to a certain, the MAE value decreases continuously
The traditional collaborative filtering algorithm is optimized by introducing the idea of interest time updating, trust level of target user and evaluation criterion differentiation
Summary
With the competition between the e-commerce enterprises becomes intensified, personalized recommendation technology, which brings great benefits to e-commerce enterprises, has caught more and more attention. The recommendation servicing has changed the shopping way of e-commerce user, from "people looks for information" to "information looks for people". Effective personalized recommendation can bring win-win situation for e-commerce enterprises and their users. It finds the latent demands of each user and recommends them to the user by the e-commerce enterprises. It improves the user's shopping experience, it creates more value for e-commerce enterprises. How to provide more accurate recommendation to users has become a problem which e-commerce enterprises cannot ignore. Because only when e-commerce enterprises understand users better than users themselves, they will become winners in the competition of e-commerce
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